TlseHypDataSet.tlse_hyp_data_set.TlseHypDataSet

class TlseHypDataSet.tlse_hyp_data_set.TlseHypDataSet(root_path: str, pred_mode: str, patch_size: int, annotations: str = 'land_cover', images: Optional[List] = None, in_h5py: bool = False, data_on_gpu: bool = False)

A torch.utils.data.Dataset object to process the Toulouse Hyperspectral Data Set

Parameters

• root_path – path to the folder where the data is stored

• pred_mode – ‘pixel’ for pixel-wise classification or ‘patch’ for patch segmentation

• patch_size – size of the patch, i.e. gives (batch_size x patch_size x patch_size x n_bands) dimensional samples

• annotations – ‘land_cover’, ‘land_use’ or ‘both’

• images – select a subset of image tiles by specifying tile index (in the following order [3d, 1c, 3a, 5c, 1d, 9c, 1b, 1e, 3e])

• in_h5py – if True, save the data samples and labels in h5py files to speed up data reading

• data_on_gpu – if True, store the whole data on the device (e.g. on the gpu)

__init__(root_path: str, pred_mode: str, patch_size: int, annotations: str = 'land_cover', images: Optional[List] = None, in_h5py: bool = False, data_on_gpu: bool = False)

Parameters

• root_path – path to the folder where the data is stored

• pred_mode – ‘pixel’ for pixel-wise classification or ‘patch’ for patch segmentation

• patch_size – size of the patch, i.e. gives (batch_size x patch_size x patch_size x n_bands) dimensional samples

• annotations – ‘land_cover’, ‘land_use’ or ‘both’

• images – select a subset of image tiles by specifying tile index (in the following order [3d, 1c, 3a, 5c, 1d, 9c, 1b, 1e, 3e])

• in_h5py – if True, save the data samples and labels in h5py files to speed up data reading

• data_on_gpu – if True, store the whole data on the device (e.g. on the gpu)

Methods

__init__(root_path, pred_mode, patch_size[, ...])	param root_path path to the folder where the data is stored
compute_patches()
compute_pixels()
load_splits([path, p_labeled, p_val, ...])
rasterize_gt_shapefile()	Rasterize the ground truth shapefile.
read_metadata()
save_data_set()
save_splits(solutions, p_labeled, p_val, ...)
split_already_computed(p_labeled, p_val, ...)

Attributes

areas
bands	return A list of usable band indices
classes
colors	return A dict of class colors for land cover maps
n_classes
n_samples
permeability	return A dict with the permeability of the land cover (0 = impermeable, 1 = permeable)
proj_data